Building an AI Search Visibility Platform for Modern Brands

DiscoveredBy was built to help brands understand how they appear across major AI engines, compare visibility against competitors, and get actionable recommendations to improve AI-generated discovery.

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Category

AI Systems / Product Engineering

Project Type

Product build in partnership with a client company

Industry

AI search intelligence, GEO, and visibility monitoring

The Problem

As AI search becomes part of how people discover brands, companies face a new visibility problem. Traditional SEO tools do not show whether ChatGPT, Gemini, Perplexity, Claude, or Grok mention a brand — or how that brand compares against competitors in AI-generated answers.

Website owners and digital marketing teams increasingly need to know how their brand appears across countries, intents, and LLM-driven search contexts, and what changes will improve that visibility.

What We Built

  • AI visibility monitoring across ChatGPT, Gemini, Perplexity, Claude, and Grok
  • competitive intelligence and share-of-voice comparison
  • daily scanning across real prompts and search contexts
  • recommendations for titles, descriptions, content, and page optimization
  • RAG-powered analysis and reporting workflows
  • agentic evaluation logic for turning observations into actionable insights

Relevant Service Lanes

DiscoveredBy is strongest as public proof for AI systems work that also requires real product engineering and user-facing delivery.

AI / RAG Knowledge Systems

Proof of retrieval-backed analysis, multi-model workflows, and grounded recommendation systems.

Open page

Product Engineering

Proof that AI logic, reporting, monitoring, and user workflows can ship as one coherent product.

Open page

Portfolio Hub

See how this AI-search product connects to the wider MicroPyramid proof set.

Open page

Why This Was Hard

This was not a traditional SEO dashboard. It had to interpret how brands appear across multiple GenAI systems, compare competitor visibility, and turn noisy AI outputs into useful recommendations.

AI search is not traditional search — each engine behaves differently and returns variable outputs

recommendations had to account for business type, geography, user persona, intent, and query aspect

the platform needed to compare visibility across multiple GenAI services under realistic prompts

raw analysis was not enough — users needed actionable recommendations they could actually use

RAG, orchestration, observability, and reporting had to work together in one product experience

Technology Stack

FastAPISvelteKitChromaDBLangfuseLangChainLangGraphCeleryAWS SQSGoogle Chrome LighthouseGeminiOpenAIClaudexAIPerplexity

Outcome

  • launched as a product for AI search visibility monitoring and optimization
  • enabled monitoring across five major GenAI services
  • combined visibility tracking, competitor comparison, and recommendations in one workflow
  • reached roughly 100 company signups in the first month

Need to Build an AI Intelligence or Recommendation Product?

If you need help turning AI visibility, recommendation logic, or multi-model analysis into a real product, MicroPyramid can help.

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